Exploration and application of sentiment analysis technology in Meituan

In May 2021, the Meituan NLP Center opened up the largest Chinese attribute-level sentiment analysis dataset ASAP based on real scenes so far. The related papers of this dataset were accepted by NAACL2021, the top conference on natural language processing, and Chinese open source data was added to the dataset. Plan Qianyan will work with other open source datasets to promote the advancement of Chinese information processing technology. This paper reviews the evolution of Meituan sentiment analysis technology and its application in typical business scenarios, including chapter/sentence-level sentiment analysis, attribute-level sentiment analysis, and opinion triplet analysis. In business applications, online real-time prediction services and offline batch prediction services are built based on the technical capabilities of sentiment analysis. Up to now, the sentiment analysis service has provided services for more than ten business scenarios within Meituan.

references

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作者介绍

任磊、佳昊、张辰、杨扬、梦雪、马放、金刚、武威等，均来自美团平台搜索与NLP部NLP中心。

招聘信息

美团搜索与NLP部/NLP中心是负责美团人工智能技术研发的核心团队，使命是打造世界一流的自然语言处理核心技术和服务能力。

NLP中心长期招聘自然语言处理算法专家/机器学习算法专家，感兴趣的同学可以将简历发送至[email protected]。具体要求如下。

岗位职责

1. 预训练语言模型前瞻探索，包括但不限于知识驱动预训练、任务型预训练、多模态模型预训练以及跨语言预训练等方向；
2. 负责百亿参数以上超大模型的训练与性能优化；
3. 模型精调前瞻技术探索，包括但不限于Prompt Tuning、Adapter Tuning以及各种Parameter-efficient的迁移学习等方向；
4. 模型inference/training压缩技术前瞻探索，包括但不限于量化、剪枝、张量分析、KD以及NAS等；
5. 完成预训练模型在搜索、推荐、广告等业务场景中的应用并实现业务目标；
6. 参与美团内部NLP平台建设和推广

岗位要求

1. 2年以上相关工作经验，参与过搜索、推荐、广告至少其一领域的算法开发工作，关注行业及学界进展；
2. 扎实的算法基础，熟悉自然语言处理、知识图谱和机器学习技术，对技术开发及应用有热情；
3. 熟悉Python/Java等编程语言，有一定的工程能力；
4. 熟悉Tensorflow、PyTorch等深度学习框架并有实际项目经验；
5. 熟悉RNN/CNN/Transformer/BERT/GPT等NLP模型并有过实际项目经验；
6. Strong sense of purpose, good at analyzing and finding problems, dismantling and simplifying, and able to discover new spaces from daily work;
7. Organized and motivated, it can sort out complicated work and establish an effective mechanism to promote upstream and downstream cooperation to achieve goals.

bonus

1. Familiar with the basic principles of each Optimizer for model training, and understand the basic methods and frameworks of distributed training;
2. Have an understanding of the latest training acceleration methods, such as mixed precision training, low-bit training, distributed gradient compression, etc.

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